This chapter introduces Python as a powerful yet beginner-friendly programming language essential for data science. It covers getting access to Python through direct installation or integrated development environments like Anaconda and Spyder. The chapter teaches fundamental programming concepts including basic operations, data types, and key data structures (lists, tuples, dictionaries, sets, and DataFrames). Students learn to write control structures using if-else statements and while/for loops, create reusable functions, and make programs interactive through user input. The chapter also explains how to install and use Python packages, which extend the language’s capabilities for specialized tasks. Throughout, practical examples demonstrate concepts like leap year calculations, temperature categorization, and sales data analysis. The chapter emphasizes Python’s accessibility, extensive package ecosystem, and suitability for data science applications, positioning it as an ideal tool for solving computational and data analysis problems.

This chapter introduces R, a free, open-source programming environment designed for data analysis and statistical computing. It covers R installation through RStudio IDE and demonstrates fundamental programming concepts including basic syntax, mathematical operations, and logical operators.

Key topics include data types (numeric, integer, character, logical, factor), data structures (vectors, matrices, lists), control structures (if-else statements, for/while loops), and functions for code organization and reusability.

The chapter emphasizes R’s advantages for data science: powerful statistical capabilities, extensive package ecosystem, and built-in data handling features. It concludes with R Markdown, which enables creation of professional reports combining code, output, and documentation in a single document for reproducible research and presentation.

This chapter argues that subjects originate internally within VP, and from there raise to spec-TP via an operation known as A Movement. The chapter begins (Module 2.1) by outlining the VP Internal Subject Hypothesis, and discussing evidence for it. Module 2.2 considers predicates, arguments, theta roles and theta marking. Module 2.3 turns to explore the syntax of unaccusative subjects, and compares unaccusatives with other type of predicate. Module 2.4 goes on to look at Passivisation, contrasting short/clause-internal and long/cross-clausal passives, and discussing constraints on Passivisation. Module 2.5 looks at the syntax of Raising structures, and compares them with Control structures, establishing criteria for determining whether a given item is a Raising or Control predicate. Module 2.6 notes that many sentences are mixed structures, containing more than one type of predicate (e.g. a passive and an unaccusative predicate). The chapter concludes with a Summary (Module 2.7), Bibliography (Module 2.8), and Workbook (Module 2.9), with some Workbook exercise examples designed for self-study, and others for assignments/seminar discussion.

This chapter reviews the basic control structures of the Python language. These are used to lay out the logic of a program so as to allow it to do an infinite amount of stuff with finite means. These structures include if, for, while, etc.